Wor, the Traditional Biak Folksongs: A Study of Their Metaphorical Expressions
Bibliographic record
Abstract
Biak is a Papuan tribe inhabiting the Biak Island, to the north of mainland Papua. This tribe has a type of folksongs called wor. Using Nöth’s theory of metaphor and Haley’s 9 categories of the hierarchy of human perceptions, this study aimed to explore the metaphorical expressions (metaphiers) found in 17 wor lyrics through metapherein process to find their metaphands, and to identify the human perceptions in the metaphiers seen through the 9 categories. The study found that the Biak tribe expresses their concerns about life and its complexity as discovered in the metaphors found in the metaphands: happiness, deliverance, caring, preservation of culture, respect, longing for homeland, perseverance, youth reminder, evil spirit, inhumane treatment to others, fighting spirit, dexterity, vigilance, strong family support, ‘heaven’, the ritual for luck, and creator of the universe. Most languages in the world must have similar universal metaphors, but their meanings may be universal or local as is true with wor lyrics. Haley’s 9 categories were found. The categories observed are: Being was found 4 times in 4 lyrics; Cosmos was observed 2 times in 2 lyrics; Energy was found 2 times in 2 lyrics; Terrestrial was discovered 2 times in 2 lyrics; Object was found 2 times in 2 lyrics; Living was not found in these present data; Animate was not observed in these current data either; and Human was observed the most, 5 times in 5 lyrics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".